Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use jkhan447/HateXplain-first-annotator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jkhan447/HateXplain-first-annotator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jkhan447/HateXplain-first-annotator")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jkhan447/HateXplain-first-annotator") model = AutoModelForSequenceClassification.from_pretrained("jkhan447/HateXplain-first-annotator") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 374c7459deeaa45b538cc19bc7f70dcdd30dbe3110f7428f12d05c1bb4703bae
- Size of remote file:
- 438 MB
- SHA256:
- ec8082fd57c28ac69af48b906bb11b68d25643bf6625aeae4d1588fa960ef144
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